In practice, machine learning models like Logistic Regression (LR), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and XGBoost (XGB) are widely used for modeling credit scoring data. However, they are affected by the missing-not-at-random bias in data to produce reliable and accurate predictions.
To tackle this problem, some existing approaches address the selection bias and conduct reject inference from multiple perspectives.
Some approaches apply the self-training algorithm [1]}, which iteratively adds rejected samples with higher default probability as default samples to retrain the model [2]}.
This is a semi-supervised approach [3]}.
Besides, Semi-Supervised SVM (S3VM) [4]} and Semi-Supervised Gaussian Mixture Models (SS-GMM) [5]} are also deployed in credit scoring systems.
In another perspective, some approaches attempt to re-weight the training approved samples to approximate unbiased data [6]}[7]}[8]}[9]}.
These approaches are similar to counterfactual learning [10]}[11]}[12]}[13]}, which attempts to re-weight observed samples to remove bias in data.
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